Enhancing Global Flood Forecasting: A Methodological Framework for Assessing High-Resolution Simulations in the DestinE G-EDT

The increasing frequency and intensity of extreme weather events highlight the urgent need for more accurate flood forecasting to mitigate the devastating impacts on communities and ecosystems. The DestinE programme aims to address this challenge by enhancing the accuracy of meteorological forecasts, particularly for extreme weather events, through higher resolution, which in turn is expected to improve flood forecasting capabilities. Nearly one year of Global Extremes Digital Twin (G-EDT) simulations, providing high-resolution meteorological data, has been generated as part of the programme. These simulations drive ECMWF’s Land Surface Modelling System (ecLand) producing runoff generation, which is then routed through the Catchment-based Macro-scale Floodplain model (CaMa-Flood) to simulate river flow. A key challenge, however, is evaluating the performance of these new high-resolution prototype systems, especially given the limited availability of long-term hindcast data for evaluation. With only a short data period available, it becomes challenging to robustly assess the predictive skill of these models in forecasting flood events.

To overcome this limitation, we have developed a methodological framework that facilitates a rigorous evaluation of these high-resolution systems, enabling meaningful assessments of their forecasting skill despite the constrained data period, focusing on the potential of the G-EDT to improve hydrological forecasting in comparison with the forecasts produced by ECMWF’s operational system at lower resolution. Specifically, the framework investigates:

• Significance Testing: Statistical testing (e.g. Student t-test) are employed to assess whether observed differences in forecast skill are statistically significant and not a result of sampling variability.
• Multi-Metric Evaluation: Beyond traditional performance scores like Kling-Gupta Efficiency (KGE), the analysis incorporates flow duration curves, bias decomposition, and regional variability assessments to capture a broader range of hydrological behaviours and extremes.
• Threshold Definition: Using six years of historical simulations, thresholds for different return periods are calculated to enable flood characterisation of different severity.
• Global and Regional Assessments: The framework evaluates performance at both global and regional scales, considering spatial variability in hydrological processes and the availability of ground-truth observations for validation.

Moreover, the results lay the foundation for a continuously evolving evaluation system for the G-EDT, designed to adapt as longer datasets become available.

The results of this analysis offer valuable insights into the benefits and limitations of using high-resolution simulations for global hydrological forecasting, particularly in the context of extreme events, and will inform future improvements of the global Extremes Digital Twin in DestinE (G-EDT).